Figuring out how to trust AI-written code faster is the project of software engineering for the next few years, IMO. We'll need to figure out the techniques and strategies that let us merge AI code sight unseen. Some ideas that have already started floating around: - Include the spec for the change in your PR and only bother reviewing that , on the assumption that the AI faithfully executed it - Lean harder on your d…
Translating from a natural language spec to code involves a truly massive amount of decision making. For a non trivial program, 2 implementations of the same natural language spec will have thousands of observable differences. Where we are today, that is agents require guardrails to keep from spinning out, there is no way to let agents work on code autonomously that won’t end up with all of those observable differenc…
I don't think "complex" is the right word here. A test suite would generally be more verbose than the implementation, but a lot of the time it can simply be a long list of input->output pairs that are individually very comprehensible and easily reviewable to a human. The hard part is usually discovering what isn't covered by the test case, rather than validating the correctness of the test cases you do have.